Contact centers, such as Automatic Call Distribution or ACD systems, are employed by many enterprises to service customer contacts. A typical contact center includes a switch and/or server to receive and route incoming packet-switched and/or circuit-switched contacts and one or more resources, such as human agents and automated resources (e.g., Interactive Voice Response (IVR) units), to service the incoming contacts. Contact centers distribute contacts, whether inbound or outbound, for servicing to any suitable resource according to predefined criteria. In many existing systems, the criteria for servicing the contact from the moment that the contact center becomes aware of the contact until the contact is connected to an agent are customer-specifiable (i.e., programmable by the operator of the contact center), via a capability called vectoring. Normally in present-day ACDs when the ACD system's controller detects that an agent has become available to handle a contact, the controller identifies all predefined contact-handling skills of the agent (usually in some order of priority) and delivers to the agent the highest-priority oldest contact that matches the agent's highest-priority skill. Generally, the only condition that results in a contact not being delivered to an available agent is that there are no contacts waiting to be handled.
The primary objective of contact center management, including call-distribution algorithms, is to ultimately maximize contact center performance and profitability. An ongoing challenge in contact center administration is monitoring of selected data entities associated with contact center operation to optimize the use of contact center resources and maximize agent performance and profitably. Current products for monitoring and reporting on contact center performance, such as Call Management System or CMS™ and Avaya IQ by Avaya, Inc., are configured as data warehouses that extract data from multiple sources, transform the data into a normalized form, and load the data into the data warehouse database, typically on a real time basis.
A common type of data warehouse is based on dimensional modeling. Dimensional modeling is a data model that divides the world into measurements and context. Measurements are usually numeric and taken repeatedly. Numeric measurements are facts. Facts are surrounded by textual context in existence when the fact is recorded. Context is often subdivided into dimensions. Fact tables are used in dimensional modeling to logically model measurements with multiple foreign keys referring to the contextual entities. The contextual entities each have an associated primary key. A “key” is a data element (e.g., attribute or column) that identifies an instance of an entity or record in a collection of data, such as a table. A “primary key” is a column or combination of columns whose values uniquely identify a row in a table or is the attribute or group of attributes selected from the candidate keys as the most suitable to uniquely identify each instance of an entity. A “foreign key” refers to a column or combination of columns whose values are required to match a primary key in another table or is a primary key of a parent entity that contributes to a child entity across a relationship. Types of primary keys include a natural key, or a key having a meaning to users, and a surrogate key, or a key that is artificially or synthetically established, meaningless to users, and used as a substitute for a natural key.
If the same entity (e.g., agent) is represented on multiple data sources (e.g., inbound call system and outbound call system) by different natural keys, a traditional data warehouse generates and assigns a surrogate key to identify the entity. The surrogate key is an internal identifier managed by the data warehouse. For example, in a contact center an agent may handle inbound calls from one system and outbound calls from another system, with different identities on each system. Data warehouses commonly process each data source independently, performing data correlation across sources at a later time.
Some data models specify a behavior known as a type 2 slowly changing dimension. A type 2 dimension tracks the history of changes to an entity over time. When an attribute of an entity is changed, such as when a contact center agent changes their skill set or group membership, a new surrogate key for that entity is generated, and a new row inserted into the database. Fact data associated with the entity can now be tracked separately for activities that occurred before versus after the change by referencing the appropriate surrogate key.
An ongoing issue confronting vendors of contact center software products is translation or conversion of selected data entities to parallel expressions. For example, contact center users and administrators desire to have data displayed in a human language of the user's choice. By way of illustration, a queue may be named the “gold” queue in English and the “oro” queue in Spanish. In administering translations and conversions, it is desirable to provide an interface that can easily update translations or conversions, add new translations or conversions, add new database items or entities, and distinguish between changes to an underlying item and changes to the translation or conversion of that item.
Current practice addresses these needs by creating additional tables attached to each column requiring translation or conversion or creating additional columns in each table. The former practice is effected using identifiers pointing from one language version to a table containing another language version. The latter practice is effected by adding separate columns or “resource bundles” for each language employed. In either case when a language is added, new database structures are created, thereby requiring release of a new software application.